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.5em plus1ex minus.2ex-.5emTIDE Error Type Analysis. In Figure REF  we show the error type analysis generated by the TIDE toolbox [1]}. A detailed description and analysis is given in the caption. The analysis reveals more granular information about where MAE and BEiT improve overall AP relative to the other initializations. In summary, we observe that all initializations lead to roughly the same classification performance for correctly localized objects, however the MAE and BEiT initializations improve localization compared to the other initializations. We observe an even stronger effect when looking at missed detections: the masking-based initializations yield notably higher recall than the other initializations and thus leave fewer undetected objects. This higher recall creates a small increase in background errors, thus leading to better overall AP.
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